• Harnessing Convolutional Neural Networks in Machine Learning for Enhanced Cancer Diagnosis
  • Zeinab Rasouli,1,* Seyed Mohammad Gheibihayat,2
    1. Faculty of Science, Department of Basic Science, Hameda Azad University, Hamedan, Iran
    2. Department of Medical Biotechnology, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran


  • Introduction: Cancer remains a leading cause of death worldwide, and early, accurate diagnosis is critical for effective treatment. Traditional diagnostic techniques, such as radiology and pathology, are often slow, human-dependent, and prone to variability. Machine learning (ML), specifically convolutional neural networks (CNNs), has emerged as a promising solution to overcome these limitations. As a type of deep learning model, CNNs excel at processing visual images, making them ideal for analyzing complex medical images associated with cancer. The increasing workload in oncology highlights the need for faster, more reliable diagnostic tools.
  • Methods: CNNs are widely used to analyze medical imaging data, including CT scans, MRIs, and mammograms. These models automatically learn and identify features, such as tumor boundaries and abnormalities, that may be missed by human observers. In addition to radiology, CNNs are being applied in digital pathology, where they analyze digitized histopathological slides to detect cancerous tissues with high accuracy. CNNs are also gaining momentum in precision oncology, where they are used to analyze genetic data and identify biomarkers associated with specific cancers, enabling personalized treatment plans.
  • Results: CNNs have demonstrated superior performance in cancer diagnostics, particularly in detecting breast cancer through mammography, especially in dense breast tissue. The models reduce observer variability, thereby lowering false-positive and false-negative rates. In digital pathology, CNNs can distinguish between benign and malignant cells, grade tumors, and predict cancer progression, such as metastasis to stage IV. Furthermore, CNNs have been instrumental in precision oncology by identifying genetic mutations, such as BRCA and EGFR, which correspond to targeted therapies that improve patient outcomes.
  • Conclusion: Despite their promise, several challenges remain in the clinical adoption of CNNs. High-quality, large datasets are necessary to train these models effectively, yet data privacy concerns and fragmented healthcare records hinder data acquisition. The "black box" nature of CNNs also poses issues for clinicians, as the rationale behind their predictions is often not transparent. Ongoing research in explainable AI (XAI) aims to make CNN models more interpretable. Collaborations between data scientists, healthcare professionals, and regulators are essential for overcoming these barriers and ensuring that AI is safely integrated into clinical practice. As AI evolves, CNNs are expected to play an even more significant role in cancer diagnosis, especially when integrated with emerging technologies like quantum computing and CRISPR gene editing.
  • Keywords: Convolutional Neural Networks, Machine Learning, Cancer Diagnosis, Medical Imaging, Digital Patholog